{"title":"图形空间采样","authors":"Li‐Chun Zhang","doi":"10.1002/sta4.708","DOIUrl":null,"url":null,"abstract":"We develop lagged Metropolis–Hastings walk for sampling from simple undirected graphs according to given stationary sampling probabilities. It is explained how the technique can be applied together with designed graphs for sampling of units‐in‐space. Compared with the existing spatial sampling methods, which chiefly focus on the sample spatial balance regardless of the associated outcomes of interest, the proposed graph spatial sampling method can considerably improve the efficiency because the graph can be designed to take into account the anticipated spatial distribution of the outcome of interest.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"24 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Graph spatial sampling\",\"authors\":\"Li‐Chun Zhang\",\"doi\":\"10.1002/sta4.708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop lagged Metropolis–Hastings walk for sampling from simple undirected graphs according to given stationary sampling probabilities. It is explained how the technique can be applied together with designed graphs for sampling of units‐in‐space. Compared with the existing spatial sampling methods, which chiefly focus on the sample spatial balance regardless of the associated outcomes of interest, the proposed graph spatial sampling method can considerably improve the efficiency because the graph can be designed to take into account the anticipated spatial distribution of the outcome of interest.\",\"PeriodicalId\":56159,\"journal\":{\"name\":\"Stat\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stat\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/sta4.708\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stat","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/sta4.708","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
We develop lagged Metropolis–Hastings walk for sampling from simple undirected graphs according to given stationary sampling probabilities. It is explained how the technique can be applied together with designed graphs for sampling of units‐in‐space. Compared with the existing spatial sampling methods, which chiefly focus on the sample spatial balance regardless of the associated outcomes of interest, the proposed graph spatial sampling method can considerably improve the efficiency because the graph can be designed to take into account the anticipated spatial distribution of the outcome of interest.
StatDecision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.10
自引率
0.00%
发文量
85
期刊介绍:
Stat is an innovative electronic journal for the rapid publication of novel and topical research results, publishing compact articles of the highest quality in all areas of statistical endeavour. Its purpose is to provide a means of rapid sharing of important new theoretical, methodological and applied research. Stat is a joint venture between the International Statistical Institute and Wiley-Blackwell.
Stat is characterised by:
• Speed - a high-quality review process that aims to reach a decision within 20 days of submission.
• Concision - a maximum article length of 10 pages of text, not including references.
• Supporting materials - inclusion of electronic supporting materials including graphs, video, software, data and images.
• Scope - addresses all areas of statistics and interdisciplinary areas.
Stat is a scientific journal for the international community of statisticians and researchers and practitioners in allied quantitative disciplines.